{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "\n",
    "centers = [\n",
    "    [-2, 2],\n",
    "    [2, 2],\n",
    "    [0, 4]\n",
    "]\n",
    "X, y = make_blobs(n_samples=60, centers=centers, random_state=0, cluster_std=0.60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.59652373 -1.08033247 -2.20874729  2.68364041 -1.54337736  1.45562098\n",
      "  1.02188099 -1.90703154  0.36844762 -0.24817139 -1.26182559 -2.53267145\n",
      " -0.762291    0.12496499 -0.24190617  0.56835118 -0.94156859 -2.06193131\n",
      " -1.10355256 -1.91357386  1.03166129  1.69420869  0.00630001 -0.63814723\n",
      " -1.41275721  0.97623789  0.89295132 -1.73368206 -1.97254489  1.12989042\n",
      "  1.12053538 -0.70387404  1.51211223  0.07614726  1.69351692  0.27939746\n",
      " -0.64245157  1.47752171 -1.81215938  1.81306848  2.43745434 -1.42994695\n",
      "  1.61940674 -0.51673541  1.24832278  1.46272006 -2.23239609  1.98309066\n",
      " -0.87946521 -0.16080202  0.21381984  2.03991033  2.24140498  0.70726774\n",
      "  1.15376522 -3.53179389  1.37086822 -0.6990899  -1.48133828  2.10645569]\n",
      "[1.7842681  2.88161526 2.09380938 1.25910451 2.07300501 2.03116724\n",
      " 2.27766935 2.22689751 4.553324   3.55152711 2.72142791 0.81152212\n",
      " 4.58163802 4.58598342 4.73346704 3.90699394 2.24009433 2.2463591\n",
      " 1.87690504 2.8725641  1.87235583 1.73715542 5.0715223  1.1273806\n",
      " 3.34453592 3.17046524 5.13753351 2.2002046  1.88768969 3.19134456\n",
      " 4.54362679 5.16617271 0.96423044 4.24119362 1.29162069 3.07825379\n",
      " 4.63267104 1.6526902  1.48754256 2.03369921 2.07738975 1.90918568\n",
      " 1.7823553  5.14603897 2.46649421 2.2321415  1.81861835 2.25699912\n",
      " 1.41363327 4.48147384 4.4239439  2.18148314 1.58911395 3.8920451\n",
      " 4.88830887 2.39217116 1.14798924 4.54049589 1.55470099 1.75893144]\n"
     ]
    }
   ],
   "source": [
    "print(X[:, 0])\n",
    "print(X[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 1, 1, 0, 2, 2])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[: 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2bde24e1a08>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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OPXb42YCNTYkJAHPu3dWTqwtXcO226i+rt1d/WD1lvLGABfb16s+rP6t2tUnjhOpZ1ROb7RgHAObHhRcMn7llS13xjbr1QcNnA7vv6qvrTX+1ewXmDb3lb+v2R9ZBBw8zF7AYlJgAMMfeXD22Wu3fJb5ZPbX6WvWLQw8FLLQ3NysnL76Z6z5a/Uz1e9UrqvuPPBcAMJwrrhgn9xtXKDGnZNt19aUvz44Eveyy2r69Nm+qI46YHf178CHrPSF7wnveU1deufacrVvr7/6uHv+EtWcBi0OJCQBz6uPV41p9gXlD/6W6W/XoQSZameXqy82OuL3+A9xHNjte8g7NjpsE5s9y9YLqhatc95nq+5vt3Hza0EMBAKNYXh4reKRcVuWaa+pD/1ynn15XXbXr644+uu5//zrmO/bcbOxZV11VH//YcHnnnFMXX1SHubcEsEJKTACYQ99qdhTs1gGynln9QHXoAFk35Yrq5dWfVJ/dxTXfuWOep1UHjjwPMKzfbPUF5vW2V0+v9q9+crCJAICx7L//OLm3GimXlTvnnNmxnyvZefeFL8wexx1XD3tY7XfL8edjz/rEx2f3tBzSGWfUQx46bCawce213gMAAKv3hmqoD0NeXL1ooKxdeUd1bPUL7brArNmOrF/Yce07R54JGM6Hql8ZIOdnmu3UBgCm7YjbDZ95i1vUIe6Vt67OOL1e99rVHx36yU/Wy18+zJGjTMuXvjQfmcDGpcQEgDn0koHzXtowuzp35n9UD211xcSXq4e0+7u6gD1nuVn5OMTpb9+onjtADgAwrqOPHidzyTuV6+Yzn6lTTtn99ZdcMitAr/vWcDOx/r761eEzL7podn9VgJXwowEAzJlLmu16GtLF1UcGzqz6o2b3yNtd/63644FmAcbxvuqTA+a9qfLhbACYtiOPnD2GdNJ9hs1j5bZcVae8Ze05F11U7//7tecwHVdfPXzmtm31LWU3sEJKTACYM6fPSe6nqucNkPPc6qwBcoBxvGrgvO3VawfOBACG9wMPGC7rdreru911uDxW5wMfqKuuGibrQ/9cl102TBbrb6+R2oOxcoGNx28XADBnvjgnuc9umCNqt+7IWm9bmu2AfW31yuot1fkNc4QmzLN/GSHzwyNkAgDDuvvd67jj1p6z1171qEc5Sna9bL22Pvax4fKWl+v004bLY30dPMJ9ag84oPbdd/hcYGPy4wEAzJmxbh2xbcCsj1YfHDDvA9XHB8xbqe3V31UPrw6svrd6YvXU6uTqTtWdq9+sLlqH+WC9XV19boTcT4yQCQAM7+GPmO2iXIsfPbkOO3yYeVi9c86prUN8+vQGPjnkvQZYV0MfG11r/z0DWCxKTACYM4fOQe6rB8y63tBHVt6cL1QPrB5Rva1dl7znV79cfUf1suzMZLFsGSl3oNPMAICRbd5cT3xS3fnOq1+77771mB+re91r8LFYha9cMHzmFVfUVd8cPpc97+53n49MYONSYgLAnDlhpNwTB8ya9+MlP1jdu3r/KtZcUf1U9ZTquhFmginaNGe5AMDw9tuvnvSkeshDa9MK/xC/y13qmc+q448fdzZu3qWXjJN7yUi57FlHH123uc1weZs3D3MMNbA49lnvAQCA1blrdWQ15Adm963uN2DeGKcHnTlC5s6cXj2s3d8J9upm/z1fOthEMF371//H3p2HWVne9x9/n1nYh50BZlhEWdzYd1wSQTTuaGoTMdFoTBPTNk2bpumvadokv/bXNF2TtIl1idbUJY1xgYjRRBNFI4IDqCBuyCIMMOz7wCzn98czKOAMzMy5n7O+X9d1LsR5ns/5XkQIcz7Pfd8MAjYEzj0jcJ4kSYpXogimToXx4+G11+CNVbBpE+xv+kt1URGUl8PgwTBhAgxwO8ms0RDyXJGj1MeUq/RKFMFFF8EDD4TJmzkTSjt88PNDtXDgICSAbmVQYlsh6Tj+sSBJUo5JAJ8DvhUw8xrCbSebBOLYOSgduxEdBK4n9a0s7wIuBq5NeSIp+00mfIk5KXCeJElKjw4dYOLE6JVMQt1haGyMSovi4kxPp+Z06HDya9qjY0y5Sr+Ro2DcOFi+PLWcU0+FSZOguhqqXoa1a2HHjg++fuRhhxEjoj9DevRM7f0k5Qe3k5UkKQd9AegeKKsI+PNAWRCVrJ0C5h0RR+bx/hF4M1DWF0lP8Spl2nU5kilJktIrkYAOHaFTZwvMbFbeP57cfuXx5CozLrschg9v//0DB0YrOu+7D+64HZYuPbbAhOiBh82bYeFC+N73YMHjcPhQanNLyn2WmJIk5aABwL8FyvoK4Vc9xbEV5JkxZB7tEPDDgHnbgEA77khZbQ7RFtehfATwmBxJkqT0GDQofGZ5eevPR1VuKCmBT3wSpkxt+72jR8OMGfDjH8Pq1a27J5mEJUvgRz+CbVvb/p6S8oclpiRJOeom4OYUMz4KfDv1UT5kcgyZcW8vuQAI/b3R3YHzpGxUCnw/C7MkSZJ0csNOge6htvlpMm5c2Dxlh5ISuOQS+MxNMGzYya+vrIRPXgdnnQUPPwyHD7f9PXftgnvuge3b2n6vpPzgmZiSJOWoBHA70Bn4z3bcfxnwU+LZpvU6otlCmhs473iLYsisAg4DHgejfPdx4Ebgv1PM+b/AmNTHkSRJUisVFUer6379qzB5HTtaYua7oUPhhhujFZKrV8OmTbBnT7R6slu3aOvYYcNgYAXs3gU//GH0tfbavx8eeghu+ZxbU0uFyBJTkqQcVgz8B/AxonMyN7binu7APwGfIypC43BkO8gVgfJGA+cFymrJazFkHiY6Y3N0DNlStrkd2A082s77/xz4i3DjSJIkqZWmTYXXXoUtW1LPuvhi6Nwl9Rxlv779oteJ/OIX7VuBebzNm+GFF+D881PPkpRb3E5WkqQ8cDmwGrgfuAjoedzXuwDnEp35uAH4A+IrMGnKbs/q0BNlxTkvwN6YcvfFlCtlmw7Az4i2qC5tw33dgR8D3yX+3+eSJEn6sOISuOaa1M+xPOssGDc+zEzKfZs3wTvvhMtb9CLU14XLk5QbLDElScoTHYm2cX0S2AGsB1YB7wJ7gIXArUBZmuY5H/hqgJyvEv8qTIh+/eLgVrIqJCXAN4ClRNvLnuj3VU/gy8BKojN+LTAlSZIyp7w/fOrT0Llz++4/4wy4+mpI+Jc6NamqCpt38CCsWhU2U1L2cztZSZLyUAIYnOkhgO8QFap3tfP+W4B/CDfOCY0Cng6cmWjKlQrN2cA9wL8RnTdbBdTwwZ9NE4GpRKvEJUmSlB0GDYLPfwF+Mb/1K+hKS+HC2TB5EiRcLqOjrFsXPnPtOhg9JnyupOxliSlJkmJTBNwBjAO+Bhxo5X1diLaW/CLpW501OYbM04FuMeRKuaIXcEnTS5IkSdmvRw+Yez2sXg1LFkdlZmPjh68rK4PxE2DiROjePf1zKrvVHYZt28Lnbt4UPlNSdrPElCRJsUoAfwRcCvwrcC8tnz9ZBtwAfAUYlpbpPnAF0AmoDZj5iYBZkiRJkpQOiQQMHx696upgyxbYsR0aGqFzJxgwAHr0dOtYtexgLSST4XP37w+fKSm7WWJKkqS0OBX4D6ItZhcTbS9Z3fS1CqLtJaeQuZWLfYjOFL07UF4p8LlAWZIkSZKUCaWl0TazgwZlehLlkqKYCu4ityyWCo4lpiRJSqtuwMymV7b5O+ARYFeArL8mKmclSZIkSSokXbpASQnU14fN7dEjbJ6k7OezC5IkSU0qgB8GyJkC/J8AOZIkSZIk5Zqi4mjb4dAG+qSwVHAsMSVJko5yHfDvKdw/FnicaDtZSZIkSZIK0YiR4TNHxpApKbtZYkqSJB3nT4BHgf5tvO8m4Fmgb/CJJEmSJEnKHRMmhD3Dsl8/GDo0XJ6k3GCJKUmS1IyrgJXA1zh5KXkx8Cvgx4BHdEiSJEmSCl23bjBteri8mbMgkQiXJyk3lGR6AEmSpGzVB/gO8E3gd0AVsBqoB3oB44EZwCmZGU+SJEmSpKx1wUfhrTdh27bUcsaMgdNPDzKSpBxjiSlJknQSnYCZTS9JkiRJknRyJaUw93q4527Ys6d9GaecApdfHnQsSTnE7WQlSWqnJLAeeAZ4ElgMHMjoRJIkSZIkSdmjVy+46WYYNKjt944dC3PnQmmH8HNJyg2uxJQkqY1eAX4EPAxsPe5rRURbjN4CfArolt7RJEmSJEmSskrPnlGR+dJL8LsXYN++E19fXg6zZsHIUemZT1L2ssSUJKmVdgF/Ctxzgmsaic5NrAL+FrgNuDr2ySRJkiRJkrJXURFMnw5TpkTnZK5dB5uq4cABSCSgRw8YOBBGjIDBQ6J/J0mWmJIktcIbwMVE28e2Vg1wDfDHwL/jHu6SJEmSJKmwFRfDGWdGL0k6GUtMSZJOYi1wAbC5nff/AEgA3ws1kCRJkiRJkhSD3bthwwao2QKHDkNJMfTpC5UV0K/cVbJKL0tMSZJOoBH4NO0vMI/4PjATuCrliaT22wksApYRrRROAEOBicAUoGPmRpMkSZIkSRmSTMLbb8GiRbBmTcvX9esHk6fAhAnRqlopbpaYkiSdwO3A84GyPg9cCHQNlCe11grgn4CfAodauKYP8Fngz4D+aZpLkiRJkiRl1v798PgvYNWqk1+7dSsseByWVsGcOdB/QPzzqbB5PJckSS1oBP41YN4W4P6AedLJ1AHfBMYD99JygQmwHfgucCbwYOyTSZIkSZKkTNu1E+68o3UF5tE2b4a77oJ3V8czl3SEJaYkSS1YCLwdOPPOwHlSSw4B1wDfAurbcN8O4Dqi8lOSJEmSJOWn2lr4yU9g16723V9XBw88AJs2hZ1LOpolpiRJLXghhswq4GAMudLRksBNwC9SyPgW8B9hxpEkSZIkSVnmqSdhx47UMurr4dFHoKEtT09LbWCJKUlSC16JIbMBWBlDrnS0nwIPBMj5KvBmgBxJkiRJkpQ9Nm6EZcvCZNXUwJIlYbKk41liSpLUgnbupnFSu2PKlQBqgT8JmPWngbIkSZIkSVJ2WLI4cN4SSDaGzZTAElOSpBaV5liuBPAQUBMw7wngnYB5kiRJkiQpcxoaYGXgbcJ27IDq6rCZElhiSpLUouE5litBmG1kj/fTGDIlSZIkSVL6bd0anWUZmiWm4lCS6QEkScpWk2LIHAhUxJArASSBOI6h8GgLSZIkqTDV1kZlR4dS6NAx09Pkr2QSNm+G99bD5i1wqBaKi6F3b6iogGHDoLRDpqdUvti2NZ7crTHlqrBZYkqS1IJLgU5E5wKGck3ALOl4O4A4vmd4PYZMSZIkSdmnoR5WrYIVK2DjRti374Ov9ewJgwbB2LFw2mmQcI+/lCWT8Nqr8OKLUYnZkk6dYNx4OO886NIlffMpP9XFsAoT4lndKVliSpLUgt7AJ4F7AmZ+MWCWdLyQhfvRDsWUK0mSJCl7rFwJv3zi2OLyaLt2Ra8VK6BvX7j8Chg6NL0z5pM9e2DeY7B69cmvra2FRS9GheflV8Dpp8c/n/JXh5hW9bpaWHHweRlJkk7gW0BZoKw/AM4MlCU1p2uO5UqSJEnKvPp6eORheOhnLReYx9u2De65G555OlpNqLbZsQPuurN1BebR9u+Hnz4ISxbHM5cKQ//ymHL7x5OrwmaJKUnSCQwBvh8gZxjwTwFypBPpCQyKIXdMDJmSJEmSMq+xISovX321ffcvXAhPPRV2pnx3qBb+5yfRSsz2WrAA3lgVbiYVlj59oi2KQ6usDJ8pWWJKknQSnwG+ncL9lcCTQPcg00gnNi2GzKkxZEqSJEnKvIXPw5tvppax6EVYuSLMPIXgqadg587Uc+bPj1ZmSm2VKILRgZ9WHjAAymNa4anCZokpSVIrfAO4m7YXkRcALwIjgk8kNe/GwHklwHWBMyVJkiRlXk0NPPdsmKzHH4cDB8Jk5bMtm2Hp0jBZBw7AwufCZKnwTJkMiUS4vKlTw+ZJR1hiSpLUSp8BVgCfBTqf5NozgDuBXwOD4x1LOsYlwPCAeZ8ABgTMkyRJkpQdfvcCNDaGyTp4EKqqwmTlsyVLwuYtXw6HD4fNVGHo2w/OOSdM1tChMHZsmCzpeJaYkiS1wWCicnIjcD/wFeBKouJoLvBd4AVgJVHZmfP/R1u3B976T6jbm+lJ1ErFwO2BsnoB/xwoS5IkSVL2qD0IKwJvAVv1MiSTYTPzSTIJr78eNvPQIXh3ddjMIPwsISd85KOpn2PZpQtcNSfaolaKg/9pSZLUDr2Ittj8Z+AxYAFwH/BVYAaQNztoVH0ZXv4jqPqTTE+iNrgA+FqKGQngLlyFKUmSJOWjDRugoSFs5u7dsGtX2Mx8smtXtGI1tOpN4TNT5mcJOaGkBK6/vv1FZteucMMN0KtX2Lmko1liSpKk5u1dDWvujf55zb3Rz5Uz/gH443beWwzcA1wdbBpJkiRJ2SSu4mtTNhZqWWL79phyt8WT225+lpBTOneBGz8DM2a07UzLkSPh81+A/j75rJhZYkqSpOat/HtINj2am2yIfq6ckQC+R7RCuC0PRZ5OtCXyDXEMJUmSJCkrHNgfT+7+ffHk5oPGwCtfjwi9ojZlfpaQc0pLYfZFUSk5YUL08+YkElF5ef2n4JPXQVlZeudUYSpJNSCRSPQhelD/MmA0UAkcBl4D7gbuTiaTgY6IliRJaXH0k5NHrLkXzvo6lJ2WmZnUZgmis1pnE52T+V/Aey1cOwG4FfgU0Ckt00mSJEnKmJjOQGnLSq5C07FjPLmdsukbOD9LyGn9+8MVV8Ill0JNDWzZAocPQXEJ9O0LAwdAx2z6700FIeUSE7gW+BGwCfgNsB7oD1wD3Alckkgkrk0mPdZZkqSccfSTk0cceYJy2o8zM5ParR/wdeCvgHeBpUAN0ZYcQ4GJRH95kyRJklQYenSPKbdHPLn5oDymb7r6Z9M3c36WkBdKSqCiInpJmRaixHwLuBJ4/OgVl4lE4q+AxcDHiQrNnwd4L0mSFLfmnpw8wicoc1oCOK3pJUmSJKlwxVVODLT0aFHnztFqtm2Bz7AcNDhsXrv5WYKkGKR8JmYymXwmmUzOP37L2GQyuRm4remnH031fSRJUpo09+TkEZ5nIUmSJEk5r7Iy/DakAwZAt25hM/PN+PFh8/r2hUGDwma2m58lSIpByiXmSdQ1/Vgf8/tIkqQQTvTk5BFr7o2ukyRJkiTlpJLS8IXa5Mlh8/LRuPFhz8acNj1LziH1swRJMYmtxEwkEiXADU0//WVc7yNJkgI60ZOTR/gEpSRJkiTlvOkzwq3G7NMHxowJk5XPunSBj30sTNaQITAhcBHdbn6WICkmca7E/A5wNrAgmUw+ebKLE4lEVXMv4PQYZ5QkSUe05snJI3yCUpIkSZJyWlkZXHJJ6jmJBMyZE63u1MmNHQejR6eW0bUrzLkaEnHvs9gafpYgKUax/DGXSCS+BHwFeAP4dBzvIUmSAmvNk5NH+ASlJEmSJOW8MWPh3HNTy7jiShg0OMw8hSCRgKvmtL/ILCuDG2+EXr3CztVufpYgKUbBS8xEIvGHwPeA14ELksnkjtbcl0wmJzb3IipCJUlSnNry5OQRPkEpSZIkSTlv5iy46GIoLm7bfZ06wbXXhj9bsxAUF8PV18CVV7btjMzRo+HWW6FfeXyztYmfJUiKWUnIsEQi8WXg34AVwKxkMlkTMl+SJMWkLU9OHnHkCcppP45nJkmSJElS7BIJmD4dTjsNnvwlvPvuya8/80y4+GIo656eGfNRIgHjJ8CoUbBsGSxdCjuaWQ7UoQOccQZMngyVg9I/5wn5WYKkmCWSyWSYoETia0TnYC4HZieTyW2BcqsmTJgwoaqqKkScJEk63t7V8ItRbf/GAyBRDJe/CWWnhZ9LkiRJkpR227bCihVQXQ1bt0J9fVSk9e8PlZUwegx0t7yMxYH9sHkL1NZGqzX79IE+vbPk7Mvj+VmCpFaaOHEiS5cuXdq0+2qbBFmJmUgkvgEUCHukAAAgAElEQVR8G6gCLmrtFrKSJCkLtOfJySN8glKSJEmS8krffvDRCzI9RWHq0hVOPTXTU7SSnyVISoOUS8xEInEjUYHZACwEvpRIJI6/bG0ymbwn1feSJEmBtef8iuOtuRfO+rpPUEqSJEmSVAj8LEFSmoRYiTms6cdi4MstXPMscE+A95IkSSGl8uTkET5BKUmSJElS4fCzBElpkvJu2slk8pvJZDJxktdHA8wqSZJCCvHk5BFr7o3yJEmSJElS/vKzBElplI1HAkuSpHQI8eTkEUeeoJQkSZIkSfnLzxIkpZElpiRJhSjkk5NH+ASlWrALeBr4AfDdph9/A+zO5FCSJEmSpLbxswRJaWaJKUlSIap+ItyTk0ckG6JcqcmvgSuBPsCFwJeArzX9OLPp318D/DZD80mSJEmS2sDPEiSlWUmmB5AkSRkw7FPQcBDq94fLLOka5argbQX+EPjZSa5rAB5pes0Fvk9UbEqSJEmSspCfJUhKM0tMSZIKUYeecOZXMz2F8tDbRKsu17fxvvuBF4m2nR0WeihJkiRJUur8LEFSmrmdrCRJkoLYAsyi7QXmEWua7t8ebCJJkiRJkiTlKldiSpKkVtsGvAAsBWqABDAEmAjMALpmbjRlWBK4FXgvxZw1RGdm3pfyRJIkSZIkScpllpiSJOmklgPfBR4C6lq4pjtwI/BVYHCa5lL2eILobMsQ7gc+C8wMlCdJkiRJkqTc43aykiSpRXXAXwOTgAdoucAE2AP8ADgb+DHRyjwVju9neV4+SgKbgDeJVrDWZ3YcSZIkSZKkoCwxJUlSs2qBq4C/BxracN8eolV0f4FFZqHYAjwZOHM+sDNwZj6oBx4FrgTKgQrgdOBUotXQM4B/x187SZKUXQ4dgl27YM8eaGzLNxeSJKmguZ2sJEn6kCRwE9EWoe31z0A/ojJT+W1JDJmNRGevzoohO1c9C9wCvNPC1w8CLza9vg78HdH5osVpmU6SJOkDySRs3ABVVbB+PezY8cHXSkpgwAAYNQrGT4CuXTM3pyRJym6WmJIk6UMebHql6q+BjwFjAmQpe62IKfc1LDEheqjgb4hKydY6APwZ8BjRys2eMcwlSZLUnO3bYN68qLxsTn09bNgQvX77W5hxDnzkfCj2U0pJknQct5OVJEnHOAR8OVBWXcAsZa8DOZaba/6SthWYR3sWmA3sDTeOJElSi159BW67reUC83gNDbDwObjjDtizO97ZJElS7rHElCRJx3gIqAmY9xvg9YB5yj6dY8rtFFNuLpkHfDfFjJeJVmVKkiTF6ZXl8Mgj0UrLttqyBe65B/b65JUkSTqKJaYkSTpGiG1k05Gp7HFmTLlnxZSbK/YAnw+UdSfwdKAsSZKk49XUwPz5qWXs3AmPPhKdpylJkgSWmJIk6ShJYEkMuS/HkKnsMTmGzAQwMYbcXPLfwOaAef8YMEuSJOmIZBLmPRZtDZuqd9+FZctSz5EkSfnBElOSJL1vN7Alhtw3YshU9qgALgic+TGgb+DMXHN74LxfAe8GzpQkSVqzBjZuDJf3wvOQbAyXJ0mScpclpiRJet+hHMtV9vijLM/LNTuAFTHkPh9DpiRJKmxVgbdd2bED1qwNmylJknKTJaYkSXpf15hyu8WUq+xxNXBxoKyrgEsCZeWquHZRWxpTriRJKkzJJKxdGz53XQyZkiQp91hiSpKk93UDTo0hd0wMmcouCeAOoDzFnArgtqa8QrY9ptxtMeVKkqTCtHcPHDgQPnfTpvCZkiQp91hiSpKkY0yLIXNqDJnKPoOBX9P+InNg0/0Dgk2Uu+L6S3pxTLmSJKkw7Y+hwATYvz+eXEmSlFssMSVJ0jE+EzivBLg+cKay12jgZeCiNt53WdN9ZwSfKDcNzbFcSZJUmBIxbZ9R5CeWkiQJS0xJklp0GPhfolJvNNAD6A4MB34f+BGwJ1PDxWgWcGbAvE8QrbBT4RgM/BL4GXD+Sa6dCTwCzCfaSlaRMUBpDLkTY8iUJEmFq0eP3MqVJEm5pSTTA0iSlG0agduBbwGbm/n6XmA1UUHzVeCPgL8FOqdrwJgVEZ1teC6QTDGrN/DPKU+kXJQAfq/p9R6wBFgJHAS6AGcDk4HKTA2Y5ToCs4EFATO7Ah8NmCdJktS5M/TqBTt3hs0d6FOQkiQJS0xJko6xnWjl4NOtvH4/8I/AY8DPCbuCMZNmAN8kKmfbqwi4C883VLQyczBwTaYHyTFfJGyJeT3RinJJkqSQRoyAxYvDZ0qSJLmdrCRJTbYTrVJqbYF5tDeA84AVIQfKsG8Af9nOe4uBe4E54caRCs4lnHw73tYqA74eKEuSJOlokyaHzRsyBMr7h82UJEm5yRJTkiSibVOvJ7UScgdwBdF2s/kgAfwD0XmFbfkM4WxgEdGvp6T2O7KauWuArH8BhgTIkSRJOl6/fjBmTLi8mbPCZUmSpNxmiSlJEnA38GSAnLW0f/VitppDtNL0X4AT7eo0FfhvoAqYlIa5pEIwnOhBgk4pZPwFcEuYcSRJkpr1sUugrCz1nKnTYOjQ1HMkSVJ+sMSUJBW8BqLzH0P5L2BjwLxs0BP4M+BNYAMwD7iDaJXYr4i24l0E3AB0yNCMUr6aDfwaaOvneR2BfwO+Q7SyWpIkKS6dO8PcudAphSevRo6E2bPDzSRJknKfJaYkqeA9DrwXMK+BqODLRwmgkmjb3FuAm4ELgd6ZHEoqAOcArwF/SnS+5YkkgMuAZcCXscCUJEnpMWAg3HRztL1sW02aDL//+1BcHH4u5Ze6w7BtG9TUwL58OctFktSikkwPIElSpoXYRra5zG/GkCupcJUB/wp8C/g58CLwCrCHaNXlKGAi8HvAsAzNKEmSClt5OfzB52HhQlj8EtTWnvj6gQPhwtlw6qnpmU+5adcuqHoZ3noLtm6FZPKDr3XpAj16QLdu0NAQfa1Ll+i/raFDoXIQJHyqT5JyliWmJKngLY0hczlQj/9HKym8MuAzTS9JkqRsU1ICF1wA554Lq16Hdeth8yY4eDAqk3r1goEVMGoUVFZaMKllhw/D00/DksXHFpdHO3Ageh1v5crox/Ly6L/Fs0f735ok5SI/W5UkFbwNMWTWAjuBduykJEmSJEk5r7QUxoyNXlJb7dgO990HO3akllNTAw8/DCtWwJVXQtduYeaTJKWHZ2JKkgpeCw90Zm2uJEmSJEn5atcuuOee1AvMo731Ftx9N+z1HE1JyimWmJKkgjcwhswOQK8YciVJkiRJyleNjfDwz+MpG7dvh/vvg4b68NmSpHi4nawkqeBNAF4OnDkGKA2cKUmSJElSPluyBN57L778zZvhuefggpnxvYfS58B+qN4EO3dEBXinzjBgAPTrC0XFmZ5OUgiWmJKkgjcLuD1wpt8PSZIkSZLUeo0N8LsX4n+f55+HyVOgm+dj5qTGRnh9ZVR4r1/f/DWdOsG48TBlCvRymywpp1liSpIK3hygHKgJlJcA/iBQliRJkiRJhWD1atizJ/73aWyEpUvh/PPjfy+FtW0rPPoobNx44utqa2HRi7BkMcycCdOmQ5EH60k5yd+6kqSC1wH4y4B5c4HTAuZJkiRJkpTv1q1L33u9sSp976UwVq+G228/eYF5tIYG+NWv4KcPQn1dfLNJio8lpiRJwJeAGQFy+gPfC5AjSZIkSVIh2bQpfe9VUwMN9el7P6Vmw3vw4ANQ184i8q234OGHIZkMO5ek+LmdrCRJQDHwU+BcoL0Pf3YBHgb6hBpKkiRJkqQCcfBg+t6roQF274HevY/997W1sKkaNm+BQ7VQXAy9+0BFBfTsCYlE+mZUpO4wPPII1KdYOq9aBcuWwYQJYeaSlB6WmJIkNRkELASuApa18d4K4CFgeuihJEmSJEkqAOkuCBsbPvjnjRtg0SJ4/fXozMzmDBwIk6fA2DFQVJyeGQXPPw87doTJeupJOOMM6Nw5TJ6k+LmdrCRJRxkMvAR8i2hl5ckUATcDK7DAlCRJkiSpvXr2TO/7deoMhw/DEwvgzjthxYqWC0yItrud91h0bc2W9M1ZyOrr4eWXw+UdOgSvLA+XJyl+lpiSJB2nFPgboBr4PnAJUH7U18uAjwDfJNp69i6gV3pHlCRJkiQpr1RUpO+9unWDoiK4525YvLht927aFBWZq1fHM5s+8M7bcOBA2MxXXgmbJylelpiSJLWgB/DHwAJgC1ALHAR2A78F/pZoC1pJkiRJkpSakSPT916DB8P990WFZHvU1cGDD0D1xrBz6VgbYvj13bIl+t9PUm6wxJQkqZU6Ap2ANB/TIUmSJElS3utXDqeckp73Ki6GjSkWZPX18MgjUG8hFps4tu1NJmHb1vC5kuJhiSlJkiRJkiRJyrhZsyAR85PDffpE51+GsG0bvLgoTJY+LK4Vk4cPx5MrKTxLTEmSJEmSJElSxg0aDNOnx5efSEBlZdjMl5dAY0PYTEVKSnIrV1J4lpiSJEmSJEmSpKwwaxaceWY82bMvgnffDZu5Zw+sWxc2U5G+/WLK7RtPrqTwLDElSZIkSZIkSVmhqBg+/nGYPiNs7kUXwVlnwr59YXMBNlaHzxRUVoTP7NMHOnYKnyspHi6cliRJkiRJkiRljaLiqHQ84wx4+teprXTs0weumgODB8M774Sb8Whba+LJLXQjRkKHDmHPsBw9OlyWpPhZYkqSJEmSJEmSss7gwfCZm6BmC7z5Fmyqhl27oLERioqgvj7azvXQoWPvKymJ7p04CU4/HYqLo39fXxfPnHUx5Ra6jh1h3DhYvDhMXnExTJgYJktSelhiSpIkSZIkSZKyVnn/6NWcZBL27mkqN5PQpQv07ROt5jxeaWk888WVK/jIR2HlSti/P0xWWVnqOZLSxzMxJUmSJEmSJEk5KZGA7j1gyFA45RQoL2++wAToVx7PDHHlKiqlr7wq+t85FUOGwDmBz1mVFD9LTEmSJEmSJElS3isri2clXmVl+Ex9YORIuCqFIrOyEj55XcvltqTsZYkpSZIkSZIkScp7iQSMHhM2s0cPGDo0bKY+bOw4uOEG6NmzbfdNngI33AidO8czl6R4WWJKkiRJkiRJkgrCpElQFPBT8dB5atkpw+DWW2HWrKg8bkkiAWecATffDJdeCh06pG9GSWGVZHoASZIkSZIkSZLSoVcvOO88ePbZ1LP69YNp01PPUet16AjnngczzoHNm2FTNezYCY2N0KkTDBwAlYOga9dMTyopBEtMSZIkSZIkSVLBOO88WL0aNmxof0ZpKcy5Gkr8hD0jioqgoiJ6ScpfLnSXJEmSJEmSJJ3U4UOwfh28+iq88gq8/Tbs25fpqdquuATmXg+Vle27v7QUrrvOAk2S4uZzIpIkSZIkSZKkZjU2wBtvwJIlsG4dJJMfvqZPH5g4EcaPh06d0z9je3TuDDfeCM88A4sWtf6+ykq4ak60lawkKV6WmJIkSZIkSZKkD9laA48+CtXVJ75u+3Z46il44QW47DI448z0zJeq0g5w8cdg9Bh4aRGsXAkNDc1fW1kJkyfD6NFQVJzeOSWpUFliSpIkSZIkSZKO8eYb8LOftVzqNWf/fvjf/4XpM2D2bEgk4psvpIoKuPoauPQy2LwJNm+G2kNQXBytMq0YCD16ZnpKSSo8lpiSJEmSJEmSpPe9+25URjY2tu/+F38HJSUwc2bYueLWsSMMPSV6SZIyryjTA0iSJEmSJEmSssPBg/DIw+0vMI9Y+BysXRtkJElSgbLElCRJkiRJkiQB8MzTsG9fmKz581IvQyVJhcsSU5IkSZIkSZLEwYOwfHm4vB074O23w+VJkgqLJaYkSZIkSZIkiZUrob4+bObyZWHzJEmFwxJTkiRJkiRJksTGDeEzN8SQKUkqDJaYkiRJkiRJkiQ2bw6fuW8f7N8fPleSlP8sMSVJkiRJkiRJHD4cU+6heHIlSfnNElOSJEmSJEmSRHFxTLkl8eRKkvKbJaYkSZIkSZIkib59w2d26ABl3cLnSpLynyWmJEmSJEmSJImKingyE34KLUlqBxfyS5IkSZIkSZI48yx4+umwmWedHTZPyjYbN8Crr0H1Rti2DerqoLQUysuhchCMHQP9B2R6Sik3WWJKkiRJkiRJkujdG4YPh3feCZPXsSOMGR0mS8o21RthwQLYuPHDX2togPXro9eLv4NTToFLL4V+5WkfU8ppLuSXJEmSJEmSJAFw0cVQXBwm68LZ0KFjmCwpWyST8NyzcOedzReYzVm7Fv7rv2DxS7GOJuUdS0xJkiRJkiRJEgD9+sGsC1PPGT4cJk5MPUfKJskkPPUk/OY30T+3RUMDPPEELHwuntmkfGSJKUmSJEmSJEl637RpcM657b9/yBC49lpIJMLNJGWDV16BRYtSy3jmGXjrzTDzSPnOElOSJEmSJEmS9L5EAi68EK68KjrXsi2mTIVPfdptZJV/9u6BXz4RJmv+fKg9GCZLymclmR5AkiRJkiRJkpR9xo+H006F55+PVqAdPtz8dYkEjBwJ55wDg4ekd0YpXRa9BIcOhcnatw+qlka/ZyS1zBJTkiRJbbIb2AAkgf5Av8yOI0mSJClG3XvApZdFKzPXr4fqati9GxqT0KULVAyMisvu3TM9qRSf+npYtjRs5stLYMYMt12WTsQSU5IkSSf1OnAb8ATwznFfGwTMBL4ATAP8/kuSJEnKPx06wvAR0UsqNJs2wcHA27/u2gU7d0DvPmFzpXzimZiSJElq0XZgLnAW8AM+XGBCtCrzXmAGMAtYk7bpJEmSJEmK36ZN8eRWV8eTK+ULS0xJkiQ1awlReflAG+75DTAaeCyWiSRJkiRJSr+9e2LK3RtPrpQvLDElSZL0IcuAC4Et7bh3P/BxYH7QiSRJkiRJyoxkjuVK+cISU5IkScfYD1wLpPKgaQPwaaKtZiVJkiRJymVlZTHldosnV8oXlpiSJEk6xt8CqwPk7Aa+GCBHkiRJkqRMGjgwntyKinhypXxhiSlJkqT37QZuC5g3H1gVME+SJEmSpHSrGAidOoXN7N4devcOmynlG0tMSZIkve8Bou1kQ7o9cJ4kSZIkSelUUgrjxoXNnDQJEjY00gn5W0SSJEnvey6GzIUxZEqSJEmSlE7TpkGHDmGyunSBiZPCZEn5zBJTkiRJ71sWQ+arQF0MuZIkSZIkpUuPnjB7dpisyy6LikxJJ2aJKUmSpPdtiyGzDtgXQ64kSZIkSek0cRJMmJhaxrnnwplnhZlHyneWmJIkSXpfcY7lSpIkSZKULokEXH4ZzDinfffOnBW9JLVOSaYHkCRJUvYYBmwJnNkbKAucKUmSJElSJiSKom1lR46Axx+HrVtPfk9FBVx2efSjpNazxJQkSdL7JgKLYshMBM6UJEmSJCmThp4Ct34R1q6F116F6mqoqYFkEoqKoH9/qKiEsWNh0KBoJaaktrHElCRJ0vuuBP4zhkxJkiRJkvJNIgHDhkUviArMZGO0WtPSUkqdZ2JKkqS8Ug38P+BSoALoDHQHxgG3AI8DDRmbLvtdCAwPmNcV+HTAPEmSJEmSslUiAUXFFphSKJaYkiQpL2wDbgSGAF8HngA2AbXAXuAV4C7gcmAE8PPMjJn1ioB/DJj310CPgHmSJEmSJEkqDJaYkiQp5z0NnAXcS+tWWa4Bfg+YCxyMca5cdQ1wXYCcqcCfB8iRJEmSJElS4bHElCRJOe0Joq1ja9px7wPAFUSrNXWsO4GPpHD/KOBRPIBdkiRJkiRJ7WOJKUmSctYa4FrgcAoZTwNfCTNOXulCVBDf3I57PwY8BwwIOpEkSZIkSZIKiSWmJEnKSUngFmB/gKwfAr8JkJNvOhOdI7oAmNiK60cAdzddXx7jXJIkSZIkScp/7vAlSZJy0nPAMwHzvg1cEDAvn1zS9FoC/BKoAt4jKpL7AxOAmUS/fj4hJ0mSJEmSpBAsMSVJUk76UeC83wKvA2cGzs0nk5tekiRJkiRJUtx8WF6SJOWcJPBUDLm/iiFTkiRJkiRJUttZYkqSpJyzFtgZQ25VDJmSJEmSJEmS2s4SU5Ik5ZzqHMuVJEmSJEmS1DaWmJIkKeckcyxXkiRJkiRJUttYYkqSpJwzMKbcATHlSpIkSZIkSWobS0xJkpRzTgV6xJA7MYZMSZIkSZIkSW1niSlJknJOApgVQ24cmZIkSZIkSZLazhJTkiTlpC8EzpsOjA2cKUmSJEmSJKl9SjI9gCRJUnvMIioeXwyU941AOZLC2wysAxqBfkRbSvs0piRJkiRJ+c3v/SVJUk4qAu4GOgXI+gxwSYAcSeEsAz4HVAIDgWnADGAE0Au4EngcaMjUgJIkSZIkKVaWmJIkKWeNAn5Can+hmQ78IMw4kgKoAX4fmADcCVQ3c80eYD5wOTAJWJ626SRJkiRJUrpYYkqSpJz2e8DDQI923HsZ8CTQLehEktprMXA28LM23LMcmExUeEqSJEmSpPxhiSlJknLeVcBKYE4rry8Hfky0kqssrqEktclyYDawtR331hNtPXt30IkkSZIkSVImWWJKkqS8UAk8ArwFfA04B+h61NeHEa3avA9YD9wEJNI8o6TmHQQ+QbRNbCpuBValPo4kSZIkScoCJZkeQJIkKaQRwHea/jlJtEKrGJ/ckrLZt4keQEjVIeCzwAv4kIIkSZIkSbnOz/MkSVLeSgCl+BceKZvtA34YMO/FppckSZIkScptfqYnSZIkKWMeIvVtZI93R+A8SZIkSZKUfpaYkiRJkjLm+RzJlCRJkiRJ6WWJKUmSJCljlsWQ+Q6wN4ZcSZIkSZKUPpaYkiRJkjJmZ0y5u2LKlSRJkiRJ6WGJKUmSJCljimPKLYkpV5IkSZIkpYclpiRJkqSMOS2GzK5AeQy5kiRJkiQpfSwxJUmSJGXMpBgyxxPfCk9JkiRJkpQelpiSJEmSMmZODJlXxZApSZIkSZLSyxJTkiRJUsZMAiYHzOsE3BQwT5IkSZIkZYYlpiRJkqSM+qeAWV8H+gTMkyRJkiRJmWGJKUmSJCmjPgJ8KUDOJOBrAXIkSZIkSVLmWWJKkiRJyrh/AT6ewv2jgPlAaZhxJEmSJElShlliSpIkScq4EuBBopWUbf0m5XJgITAg9FCSJEmSJCljLDElSZIkZYUS4DvA74DZrbj+bOB/gHlAvxjnkiRJkiRJ6VeS6QEkSZIk6WhTgaeAt4EngCpgPdAAlAMTiM7RnAEkMjSjJEmSJEmKlyWmJEmSpKw0ouklSZIkSZIKj9vJSpIkSZIkSZIkScoqlpiSJEmSJEmSJEmSsoolpiRJkiRJkiRJkqSsYokpSZIkSZIkSZIkKauUZHoASZIkSZIkSZIyKZmEmhrYuBG2boW6OigthX79oLISysshkcj0lJJUWCwxJUmSJEmSJEkFqbERli+HxS/Bli0tX9e/P0yZCuPGQZH7G0pSWlhiSpIkSZIkSZIKzvbt8Nij8N57J792yxaYPw+WL4Or5kCfPvHPJ0mFzmdGJEmSJEmSJEkFZeNGuPOO1hWYR3vvvei+jRvjmUuS9AFLTEmSJEmSJElSwdi5A/7nJ1Bb2777a2uj+3fuDDuXJOlYlpiSJEmSJEmSpIKQbIR589pfYB5RWwvzHovyJEnxsMSUJEmSJEmSJBWEla/D2rVhstauhddfD5MlSfowS0xJkiRJkiRJUkFYsjhw3pKweZIyL9kI27dHZ+Bu2AD79mV6osJVkukBJEmSJEmSJEmK2549sH592Mx162DvHijrHjZXUno1NsLbb0PVy9Hv68OHj/16WRmMGAmTJ8OAAZmZsRBZYkqSJEmSJEmS8l51dXy5oywxpZxVXQ2PPQo1NS1fs3cvLK2KXmefDZdcAl26pm/GQmWJKUmSJEmSJEnKe9u2xZO7dRuMiidaUsyWLIEnFkAy2fp7VqyANWtg7vVQURHfbPJMTEmSJEmSJElSAWioz61cSfGqehkWPN62AvOI/fvhJ/fCli3h59IHLDElSZIkSZIkSXmvQ4fcypUUny2bYcGC1DJqa+HnD0F9XZiZ9GGWmJIkSZIkSZKkvFfeP57c/jHlSopHMgnz5kFjY+pZW7fCwudTz1HzLDElSZIkSZIkSXmvogISibCZiQQM9Ew8KaesWwvV1eHyFr8Eda7GjIUlpiRJkiRJkiQp73XuDKNGhc08/fQoV1LuWLo0bF5tLbyxKmymIpaYkiRJkiRJkqSCMHVa4LypYfMkxW/9+tzIlCWmJEmSJEmSJKlAnHIKjBsXJmvcOBh6SpgsSelx8ADs3h0+d/Pm8JmyxJQkSZIkSZIkFZCLL4a+fVPL6Ns3ypGUWw7WxpR7MJ7cQmeJKUmSJEmSJEkqGJ06w6dvaH+R2bdvdH8nz8KUck5RTK1YXLmFzl9WSZIkSZIkSVJB6d4dbvkcTJrUtvsmTYbPfS66X1LuKSuD4uLwub16hc8UlGR6AEmSJEmSJEmS0q1jR7jschg/AZYshhUroL7+w9eVlMDZZ8PkKVBRkf45JYVTXAwDBsDGjWFzB/pnQywsMSVJkiRJkiRJBauiAq6aA5dfAVtroKYG6uqhtATKy6FfeTwrtyRlxqjTw5eYp58eNk8RS0xJkiRJkiRJUsErLoYBA6OXpPw1YTw8+1toaAiTN3hwtLpT4XkmpiRJkiRJkiRJkgpC125wzjnh8mbPDpelY1liSpIkSZIkSZIkqWCcf36Y1ZPTZ8DgIannqHmWmJIkSZIkSZIkSSoYxSUwdy707t3+jLPPhgsvDDeTPswSU5IkSZIkSZIkSQWlrDvcdDMMH962+xIJOO88uPoaKLJli1VJpgeQJEmSJEmSJEmS0q1bN5h7Pbz6CrzwAmzdeuLrhw+HCy6Aisr0zFfoLDElSZIkSZIkSZJUkBIJGDsOxoyF99bD2nWweRPs3x+ttOzRAwZWwIgRqW0/q7azxJQkSZIkSZIkSVJBSyRgyLxqJ3IAABTiSURBVNDopezgbr2SJEmSJEmSJEmSsoorMSVJkiRJkiQph9UdhoZGKC2F4uJMTyNJUhiWmJIkSZIkSZKUQ+rrYOXrsOp1qK6GvXujf59IQHk5DBoME8ZDRWVm55QkKRWWmJIkSZIkSZKUA5JJWL4cfv0rOHCg+a9v2RK9ql6GIUPgiiugb7/0zypJUqo8E1OSJEmSJEmSstzhQ/DTB2HeY80XmM1Zvx5uuw2WLo13NkmS4uBKTEmSJEmSJEnKYnWH4f77Yd26tt/b0ADz50FDPUyeEn42SZLiYokpSZIkSVloB1AFvAckgXJgAlABJDI4lyRJSr+nnmpfgXm0J56AgRUwaFCYmSRJipslpiRJkiRliUPAg8APgcUtXDMC+DzwWf5/e/ceNFddnwH8+SaBSNDgJSiIVATFaFFQUPHGRQVvU0SLtXbqrdWK1XppnWod673Vzjitl9G24wWmBds6pUIr9dKpYhFbFRQZBRQvKEhEuYkBKob8+sduOjGTN7xvcnbP2X0/n5mdzZ7dffLlD85uzrPnd5I7T2kuAKA/l38vOf/8Xc9pLTnrzOTkk5OVjgoDMANcExMAAGAAvpzRmZbPz8IFZpJcluTVSdYnOWvyYwEAPTv33O6yrrkmueSS7vIAYJKUmAAAAD07Lckjk1y8hPdcneTEJK/LaLlZAGD+XHdd8t3vdpvZxVmdADANSkwAAIAefSzJc5PctpPvf3uSt3U3DgAwIJd/r/vMK65INv2i+1wA6JoSEwAAoCcbMrq25a6eSfmmJP+zy9MAAEOzYUP3mZs3J1f/uPtcAOiaEhMAAKAnr05yfQc5m5O8KJaVBYB5s3HjZHJvmlAuAHRJiQkAANCDq5L8U4d5X0/ymQ7zAID+VU0qeEK5ANAhJSYAAEAPTs/OXwdzIad0nAcA9Gvt2tnKBYAuKTEBAAB6MIlrWH5xApkAQH/23bf7zJUrk7337j4XALqmxAQAAOjBRRPI/HaSmyeQCwD048ADu19S9sADR0UmAAydEhMAAKAHG2csFwCYvjutTdav7zbzYQ/rNg8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      "text/plain": [
       "<Figure size 2304x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10), dpi=144)\n",
    "\n",
    "c = np.array(centers)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=100, cmap='cool')\n",
    "plt.scatter(c[:, 0], c[:, 1], s=100, marker='^', c='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "k = 5\n",
    "clf = KNeighborsClassifier(n_neighbors=k)\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 2]]\n",
      "[0]\n",
      "[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[16, 20, 48,  6, 23]], dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sample =np.array([0, 2]).reshape(1, -1)\n",
    "print(X_sample)\n",
    "print(X_sample[:, 0])\n",
    "\n",
    "y_sample = clf.predict(X_sample)\n",
    "print(y_sample)\n",
    "neighbors = clf.kneighbors(X_sample, return_distance=False)\n",
    "\n",
    "neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10), dpi=144)\n",
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=100, cmap='cool')\n",
    "plt.scatter(c[:, 0], c[:, 1], s=100, marker='^', c='k')\n",
    "\n",
    "plt.scatter(X_sample[:, 0], X_sample[:, 1], marker=\"x\", c=y_sample, s=100, cmap='cool')\n",
    "\n",
    "for i in neighbors[0]:\n",
    "    plt.plot(\n",
    "        [X[i][0], X_sample[:, 0]],\n",
    "        [X[i][1], X_sample[:, 1]],\n",
    "        'k--',\n",
    "        linewidth=0.6\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "n_dots = 40\n",
    "X = 5 * np.random.rand(n_dots, 1)\n",
    "y = np.cos(X).ravel()\n",
    "\n",
    "y += 0.2 * np.random.rand(n_dots) - 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                    metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                    weights='uniform')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "k = 5\n",
    "knn = KNeighborsRegressor(k)\n",
    "knn.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9862445871129419"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T = np.linspace(0, 5, 500)[:, np.newaxis]\n",
    "y_pred = knn.predict(T)\n",
    "knn.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'KNeighborsRegressor (k = 5)')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10), dpi=144)\n",
    "\n",
    "plt.scatter(X, y, c='g', label='data', s=100)\n",
    "plt.plot(T, y_pred, c='k', label='prediction', lw=4)\n",
    "plt.axis('tight')\n",
    "\n",
    "plt.title(\"KNeighborsRegressor (k = %i)\" % k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset shape (768, 9)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('datasets/pima-indians-diabetes/diabetes.csv')\n",
    "print('dataset shape {}'.format(data.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome\n",
       "0    500\n",
       "1    268\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"Outcome\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of X (768, 8); shape of Y (768,)\n"
     ]
    }
   ],
   "source": [
    "X = data.iloc[:, 0:8]\n",
    "Y = data.iloc[:, 8]\n",
    "\n",
    "print('shape of X {}; shape of Y {}'.format(X.shape, Y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name : KNN; socre : 0.6623376623376623.\n",
      "name : KNN with weights; socre : 0.6623376623376623.\n",
      "name : Radius Neighbors; socre : 0.6103896103896104.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier, RadiusNeighborsClassifier\n",
    "\n",
    "models = []\n",
    "models.append((\"KNN\", KNeighborsClassifier(n_neighbors=2)))\n",
    "models.append((\"KNN with weights\", KNeighborsClassifier(n_neighbors=2, weights=\"distance\")))\n",
    "models.append((\"Radius Neighbors\", RadiusNeighborsClassifier(n_neighbors=2, radius=500.0)))\n",
    "\n",
    "results = []\n",
    "for name, model in models:\n",
    "    model.fit(X_train, Y_train)\n",
    "    results.append((name, model.score(X_test, Y_test)))\n",
    "\n",
    "for i in range(len(results)):\n",
    "    print(\"name : {}; socre : {}.\".format(results[i][0], results[i][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name : KNN, cross val score : 0.7147641831852358\n",
      "name : KNN with weights, cross val score : 0.6770505809979495\n",
      "name : Radius Neighbors, cross val score : 0.6497265892002735\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "results = []\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=10)\n",
    "    cv_result = cross_val_score(model, X, Y, cv=kfold)\n",
    "    \n",
    "    results.append((name, cv_result))\n",
    "\n",
    "for i in range(len(results)):\n",
    "    print(\"name : {}, cross val score : {}\".format(results[i][0], results[i][1].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train score : 0.8485342019543974, test score : 0.6623376623376623.\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=2)\n",
    "knn.fit(X_train, Y_train)\n",
    "train_score = knn.score(X_train, Y_train)\n",
    "test_score = knn.score(X_test, Y_test)\n",
    "\n",
    "print(\"train score : {}, test score : {}.\".format(train_score, test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'H:\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from common.utils import plot_learning_curve\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=2)\n",
    "cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)\n",
    "\n",
    "plt.figure(figsize=(10, 6), dpi=200)\n",
    "plot_learning_curve(plt, knn, \"Learn Curve for KNN Diabetes\", X, Y, ylim=(0.0, 1.01), cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[148. ,  33.6],\n",
       "       [ 85. ,  26.6],\n",
       "       [183. ,  23.3],\n",
       "       [ 89. ,  28.1],\n",
       "       [137. ,  43.1]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "selector = SelectKBest(k=2)\n",
    "X_new = selector.fit_transform(X, Y)\n",
    "X_new[0: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name : KNN, cross val score : 0.725205058099795\n",
      "name : KNN with weights, cross val score : 0.6900375939849623\n",
      "name : Radius Neighbors, cross val score : 0.6510252904989747\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=10)\n",
    "    cv_result = cross_val_score(model, X_new, Y, cv=kfold)\n",
    "    \n",
    "    results.append((name, cv_result))\n",
    "\n",
    "for i in range(len(results)):\n",
    "    print(\"name : {}, cross val score : {}\".format(results[i][0], results[i][1].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2bdf278af08>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=200)\n",
    "\n",
    "plt.ylabel(\"BMI\")\n",
    "plt.xlabel(\"Glucose\")\n",
    "\n",
    "plt.scatter(X_new[Y==0][:, 0], X_new[Y==0][:, 1], c='r', s=20, marker='o')\n",
    "plt.scatter(X_new[Y==1][:, 0], X_new[Y==1][:, 1], c='r', s=20, marker='^')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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